The role of the orbitofrontal cortex in human adaptive learning under strategic environments

This paper proposes an augmented learning model from a neuroscience perspective. This model contains brain activity data of the orbitofrontal cortex as a predictive variable of human strategic behavior. A Bayesian 3-layer perceptron, which shows the complex relationship between decision factors, was adopted to describe the learning behavior. However, the model's complexity creates the possibility of overtting. To avoid this problem, we adopt the Bayesian estimation and Akaike's Bayesian information criteria, which provide the statistical basis of the model selection, to select the model. Our experience shows that this model can better predict human strategic behavior than do existing behavioral learning models.neuroeconomics, learning model, orbitofrontal cortex, neural network,

Visualizing the Periods of Stock Prices Using Non-Harmonic Analysis of the NASDAQ Composite Index Since 1985

Abstract: The prediction of stock prices is studied extensively, because of the demand from private investors and financial institutions. However, long-term prediction is difficult due to the large number of factors that affect the real market. Previous research has focused on the fluctuation patterns and fluctuation periodicity of stock prices. We have likewise focused on the periodicity of stock prices. We have used a new high-resolution frequency analysis (non-harmonic analysis) method can solve the previous problem of the frequency resolution being low. As a consequence, we have succeeded in visualizing the various periodicities of stock prices. The periodicity fluctuates gently in many periods, but we have confirmed that it fluctuated violently in periods when a sudden event occurred. We expect that this experimental result in combination with previous research will help increase predictive accuracy and will aid long-term prediction

強化学習型マルチエージェントによる交通信号制御に関する研究

取得学位：博士(工学)，学位授与番号：博甲第669号，学位授与年月日：平成16年3月25日,学位授与年：200